Privacy preserving in banking sector

Virupaksha, Shashidhar; Bhramaramba, V.

doi:10.1109/icatcct.2016.7912065

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…In a financial context, the wide diffusion of cloud computing has caused an increase in the number of restrictions that financial firms apply to cloud applications 26 . To this aim, many solutions have been proposed and implemented to protect financially sensitive data against unauthorized access 27‐29 . One of the most recent one is the developing of a cryptographic‐based model sharing technique to securely outsource knowledge reflected in decision trees of multiple parties, designing a secure computation mechanism to facilitate privacy‐preserving knowledge transfer 30 …”

Section: Related Workmentioning

confidence: 99%

“…26 To this aim, many solutions have been proposed and implemented to protect financially sensitive data against unauthorized access. [27][28][29] One of the most recent one is the developing of a cryptographic-based model sharing technique to securely outsource knowledge reflected in decision trees of multiple parties, designing a secure computation mechanism to facilitate privacy-preserving knowledge transfer. 30 The need for privacy preservation is very important also in the field of social networks, where billions of users share their personal data with various devices over the Internet on a daily basis.…”

Section: Motivations For Privacy Preserving Recommending Systemsmentioning

confidence: 99%

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Dare‐to‐Share: Collaborative privacy‐preserving recommendations with (almost) no crypto

Russo

Bracciale

Bianchi

2021

Security and Privacy

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Collaborative recommending systems aim to predict a potential user‐item rating on the basis of remaining ones. Since, in several contexts, sharing of other users' ratings may be prevented by confidentiality concerns, several works have effectively addressed the design of privacy preserving recommenders. Still, most of the proposed solutions rely on advanced cryptographic methodologies, whose may conflict with the simplicity and viability requirements of real world deployments. In contrast, we propose an approach which does not require any complex cryptography. We show that whenever we can tolerate recommendations based on average values, we can transform the recommender into a privacy‐preserving one, by using two noncolluding replicas of the same system, and by distributing randomly “blinded” data to these replicas. To protecting each user's rating, a key asset of our approach is the ability to conceal which specific items are rated by which users. Our proposal is secure under the honest‐but‐curious attacker's assumption, and we show how it can be extended to guarantee robustness also against malicious adversaries. Finally, as a proof‐of‐concept, we present an implementation of the proposed approach for our motivating use case—collaborative assessment of computer/network vulnerabilities without revealing which of them affect one own infrastructure.

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Section: Related Workmentioning

confidence: 99%